CPS: Synergy: Sensor Network-Based Lower-Limb Prosthetic Optimization and Control

在美国，有超过一百万人，包括许多在最近的军事任务中受伤的战士，生活在下肢截肢的情况下。本计画将设计可穿戴式人体面积感测器系统，以即时量测截肢者的能量消耗，并将发展电脑演算法，以自动化下肢义肢的最佳化。这项技术将为假肢的优化调整提供一个实用的工具，可以最大限度地减少截肢者在行走过程中的能量消耗。此外，本计画将发展使用者控制技术，以支援使用者对下肢义肢的意志控制。开发的意志控制技术将使假肢能够适应改变的环境和情况，使截肢者能够使用自己的生物肢体行走。具有用户控制能力的优化假肢将增加完好肢体和假肢上的相等力分布，并降低肌肉骨骼失衡或病理对完好肢体造成损伤的风险。保持这些区域的健康对截肢者的生活质量和福祉至关重要。 这项研究将通过传感器和计算技术的集成来优化和控制物理系统，从而推进网络物理系统（CPS）科学和工程。该项目将设计身体区域传感器网络系统，该系统集成来自肌电图（EMG），脑电图（EEG）和惯性测量单元（IMU）传感器的时空信息，为个性化假肢优化提供定量，实时测量用户的身体负荷和精神努力。本项目将设计基于机器学习技术的假肢参数自动优化技术，支持用户自行进行家庭假肢优化。本计画亦将发展一种以脑电为基础，以嵌入式计算为支援的意志控制技术，以支援使用者？他们的意志控制的假肢实时通过他们的思想，以科普变化的情况和环境。该项目的技术进步将为医疗保健和人类CPS交互应用中的更广泛应用提供可穿戴和无线身体区域传感解决方案。探索的计算方法将广泛适用于实时，自动目标识别的时空，多变量数据在CPS相关的通信和控制应用。这个协同项目将在计算机科学家和工程师、临床医生和假肢行业工程师之间的多学科团队合作下实施。该项目还将为本科生和研究生提供跨学科的CPS相关培训，将计算方法与传感器网络，嵌入式处理器，人类身心活动识别和假肢控制相结合。

More than one million people including many wounded warfighters from recent military missions are living with lower-limb amputation in the United States. This project will design wearable body area sensor systems for real-time measurement of amputee's energy expenditure and will develop computer algorithms for automatic lower-limb prosthesis optimization. The developed technology will offer a practical tool for the optimal prosthetic tuning that may maximally reduce amputee's energy expenditure during walking. Further, this project will develop user-control technology to support user's volitional control of lower-limb prostheses. The developed volitional control technology will allow the prosthesis to be adaptive to altered environments and situations such that amputees can walk as using their own biological limbs. An optimized prosthesis with user-control capability will increase equal force distribution on the intact and prosthetic limbs and decrease the risk of damage to the intact limb from the musculoskeletal imbalance or pathologies. Maintenance of health in these areas is essential for the amputee's quality of life and well-being. Student participation is supported.This research will advance Cyber-Physical Systems (CPS) science and engineering through the integration of sensor and computational technologies for the optimization and control of physical systems. This project will design body area sensor network systems which integrate spatiotemporal information from electromyography (EMG), electroencephalography (EEG) and inertia measurement unit (IMU) sensors, providing quantitative, real-time measurements of the user's physical load and mental effort for personalized prosthesis optimization. This project will design machine learning technology-based, automatic prosthesis parameter optimization technology to support in-home prosthesis optimization by users themselves. This project will also develop an EEG-based, embedded computing-supported volitional control technology to support user?s volitional control of a prosthesis in real-time by their thoughts to cope with altered situations and environments. The technical advances from this project will provide wearable and wireless body area sensing solutions for broader applications in healthcare and human-CPS interaction applications. The explored computational methods will be broadly applicable for real-time, automatic target recognition from spatiotemporal, multivariate data in CPS-related communication and control applications. This synergic project will be implemented under multidisciplinary team collaboration among computer scientists and engineers, clinicians and prosthetic industry engineers. This project will also provide interdisciplinary, CPS relevant training for both undergraduate and graduate students by integrating computational methods with sensor network, embedded processors, human physical and mental activity recognition, and prosthetic control.